Characterizing semantic compositions in the brain: A model-driven fMRI re-analysis
Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house"). So far, neuroimaging studies have mostly relied on high-level contrasts (e.g., meaningful > non-meaningful phrases) to identify brain regions sensitive to semantic composition. However, such an approach is less apt at addressing how composition is carried out, namely what functions best characterize the integration of constituent concepts. To address this limitation, we rely on simple computational models to explicitly characterize alternative compositional operations, and use representational similarity analysis to compare the representations of models to those of target regions of interest within the general semantic network. To better target composition beyond specific task demands, we re-analyze fMRI data aggregated from four published studies (N = 85), all employing two-word combinations but differing in task requirements. Converging evidence from confirmatory and exploratory analyses reveals compositional representations in the pars triangularis of the left inferior frontal gyrus (BA45), even when analyses are restricted to a subset where the task did not require participants to actively engage in semantic processing. These results suggest that BA45 represents combinatorial information automatically across task demands, and further characterize these combinatorial representations as resulting from the (symmetric) intersection of constituent features. Additionally, a cluster of compositional representations emerges in the left middle superior temporal sulcus, while semantic, but not compositional, representations are observed in the left angular gyrus. Overall, our work clarifies which brain regions represent semantic information compositionally across different contexts and task demands, and qualifies which operations best describe composition.